Incentive Mechanism for Privacy-Preserving Collaborative Routing Using Secure Multi-Party Computation and Blockchain.

Traffic congestion results from the spatio-temporal imbalance of demand and supply. With the advances in connected technologies, incentive mechanisms for collaborative routing have the potential to provide behavior-consistent solutions to traffic congestion. However, such mechanisms raise privacy concerns due to their information-sharing and execution-validation procedures. This study leverages secure Multi-party Computation (MPC) and blockchain technologies to propose a privacy-preserving incentive mechanism for collaborative routing in a vehicle-to-everything (V2X) context, which consists of a collaborative routing scheme and a route validation scheme. In the collaborative routing scheme, sensitive information is shared through an off-chain MPC protocol for route updating and incentive computation. The incentives are then temporarily frozen in a series of cascading multi-signature wallets in case vehicles behave dishonestly or roadside units (RSUs) are hacked. The route validation scheme requires vehicles to create position proofs at checkpoints along their selected routes with the assistance of witness vehicles using an off-chain threshold signature protocol. RSUs will validate the position proofs, store them on the blockchain, and unfreeze the associated incentives. The privacy and security analysis illustrates the scheme's efficacy. Numerical studies reveal that the proposed incentive mechanism with tuned parameters is both efficient and implementable.

Incorporating Situation Awareness Cues in Virtual Reality for Users in Dynamic in-Vehicle Environments.

The increasing ubiquity and mobility of virtual reality (VR) devices has introduced novel use cases, one of which is using VR in vehicles, both human-driven and fully automated. However, the effects of the adoption of VR-in-the-car on user task performance, safety, trust, and perceived risk are still largely unknown or not fully understood. Blocking out the physical world and substituting it with a virtual environment has many potential benefits including fewer distractions and greater productivity. However, one shortcoming of this seclusion is losing situation awareness which becomes critical in dynamic, in-vehicle environments, even when the user is not in the driver's seat. Hence, this study aims to understand the effects of providing VR users with situation awareness cues about the real world, when riding in a human-driven or a fully automated car. The results of this driving simulator experiment provide valuable insights into passengers' experience and their information needs while immersed in VR environments. Identifying passengers' unique challenges and needs, as well as developing solutions for them, is expected to improve users' travel experience towards a wider adoption of VR devices.

Impacts of Pokémon GO on route and mode choice decisions: exploring the potential for integrating augmented reality, gamification, and social components in mobile apps to influence travel decisions.

This study aims to understand the impacts of Pokémon GO, a popular location-based augmented reality (AR) mobile gaming app, on route and mode choices. Pokémon GO leverages AR to introduce virtual objects at fixed and dynamic locations that translate through the app interface to incentives in the real world that potentially influence users' route and mode choices. Its gaming nature and social components can possibly enhance long-term user engagement through applying the characteristics of game elements and providing opportunities for competition, collaboration, companionship, and social reinforcement. An online survey is conducted to collect the self-reported behavior of a group of Pokémon GO users to explore its impacts on the following aspects of travel behavior: (1) the frequency of changing the route to interact with virtual objects; (2) the likelihood of carpooling more instead of driving alone for more in-app collaboration; and (3) the likelihood of shifting mode from drive alone to public transit, walking, and cycling if provided with additional incentives. The ordered survey responses including frequency and likelihood are analyzed using random parameters ordered probit models to account for the unobserved heterogeneity across users and identify subpopulations of travelers who are more susceptible to the influence of Pokémon GO. The modeling results identify four types of variables (attitude and perceptions related to Pokémon GO, app engagement, play style, and sociodemographic characteristics) that affect users' travel behavior. The results illustrate that such apps with integrated AR, gamification, and social components can be used by policymakers to influence various aspects of travel behavior. The study findings and insights can provide valuable feedback to system operators for designing such apps to dynamically manage traffic in real-time and promote long-term sustainable mode shifts.

Safety and health perceptions of location-based augmented reality gaming app and their implications.

This study seeks to understand the potential safety and health implications of location-based augmented reality gaming apps ("LAR apps") through studying people perception of Pokémon GO, a popular LAR gaming app. These perceptions can affect app usage behavior, app retention rate, and market share which can be critical to policymakers and app developers. An online survey is conducted to capture the impacts of Pokémon GO regarding: (i) perceived risk of using the app and opinion of prohibiting its usage while driving and cycling, (ii) frequency of app-related distracted driving and cycling, (iii) frequency of app-induced driving and potentially unsafe driving behavior, (iv) average daily steps before and after using the app, and (v) perceived physical and mental health benefits. Multivariate binary probit models and random parameters ordered probit models were estimated to capture users' and non-users' characteristics that affect these perceptions, attitude, and behavior. The results suggest that LAR gaming apps can potentially promote physical activity by encouraging people to walk more, increase social interactions such as app-related discussions, but also contribute to increased app-related distracted driving and cycling, app-induced driving, and unsafe driving behavior. The study findings and insights can provide valuable feedback to legislators and LAR gaming app developers for designing policies and app mechanisms that can address the safety concerns of using such apps, and provide physical and mental health benefits to its users.

A Self-Adaptive Dynamic Recognition Model for Fatigue Driving Based on Multi-Source Information and Two Levels of Fusion.

To improve the effectiveness and robustness of fatigue driving recognition, a self-adaptive dynamic recognition model is proposed that incorporates information from multiple sources and involves two sequential levels of fusion, constructed at the feature level and the decision level. Compared with existing models, the proposed model introduces a dynamic basic probability assignment (BPA) to the decision-level fusion such that the weight of each feature source can change dynamically with the real-time fatigue feature measurements. Further, the proposed model can combine the fatigue state at the previous time step in the decision-level fusion to improve the robustness of the fatigue driving recognition. An improved correction strategy of the BPA is also proposed to accommodate the decision conflict caused by external disturbances. Results from field experiments demonstrate that the effectiveness and robustness of the proposed model are better than those of models based on a single fatigue feature and/or single-source information fusion, especially when the most effective fatigue features are used in the proposed model.